Indonesian people mostly tend to ignore health, especially dengue fever because the symptoms that arise are similar to common fever, this causes people with common fever symptoms to be reluctant to see a doctor. This study aims to help the public recognize the symptoms of dengue fever early by using the implementation of the fuzzy method in the dengue fever disease diagnosis system that uses specific datasets as a reference. Mamdani's fuzzy method, which is based on linguistic frameworks and fuzzy concepts, allows for the management of knowledge from experts for intuitive decision-making. The test results using the Confusion Matrix, the system showed an accuracy of 92.8%, an average precision of 90.6%, a recall of 95.8%, and an F1-Score of 92.6%, with an effectiveness value of 100%. This study proves that the system is able to provide information about dengue fever, diagnose based on symptoms, and offer solutions for its treatment.

Children under five are more at risk of experiencing nutritional disorders because during this period children will experience very rapid growth and development. Nutritional disorders can result in children's performance in activities not being optimal, hampering the growth and development process, and can even cause disease. Malnutrition sufferers in Indonesia are increasing every year, and the percentage of malnutrition in Indonesia is around 3.4%. An expert system is a computer-based system built based on facts, knowledge and reasoning that can help solve a problem. However, to measure the uncertainty and level of expert confidence in identifying and detecting malnutrition, an expert system can use the Dempster-Shafer method. The results of testing the system model using the Dempster-Shafer method on toddler nutritional diseases obtained quite good results with system accuracy of 84% with a precision value of 81.83% and also a recall value of 80%.